THE POWER OF TV: CABLE TELEVISION AND WOMEN S STATUS IN INDIA

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1 THE POWER OF TV: CABLE TELEVISION AND WOMEN S STATUS IN INDIA Robert Jensen School of Public Affairs, UCLA, Watson Institute for International Studies, Brown University and NBER Emily Oster University of Chicago and NBER Forthcoming, Quarterly Journal of Economics Abstract Cable and satellite television have spread rapidly throughout the developing world. These media sources expose viewers to new information about the outside world and other ways of life, which may affect attitudes and behaviors. This paper explores the effect of the introduction of cable television on women s status in rural India. Using a three-year, individual-level panel dataset, we find that the introduction of cable television is associated with significant decreases in the reported acceptability of domestic violence towards women and son preference, as well as increases in women s autonomy and decreases in fertility. We also find suggestive evidence that exposure to cable increases school enrollment for younger children, perhaps through increased participation of women in household decision-making. We argue that the results are not driven by pre-existing differential trends. 1 Introduction The growth of television in the developing world over the last two decades has been extraordinary. Estimates suggest that the number of television sets in Asia has increased more than six-fold, from 100 million to 650 million, since the 1980s (Thomas, 2003). In China, television exposure grew from 18 million people in 1977 to 1 billion by 1995 (Thomas, 2003). In more recent years, satellite and cable television availability has increased dramatically. Again in China, the number of people with satellite access increased from just 270,000 in 1991 to 14 million by Further, these numbers are likely to understate the change in the number of people for whom television is available, since a single television is often watched by many. Several studies have demonstrated that the information and exposure provided by television can influence a wide range of attitudes and behavior. Gentzkow and Shapiro (2004) find that Matthew Gentzkow, Larry Katz, Steve Levitt, John List, Divya Mathur, Ben Olken, Andrei Shleifer and Jesse Shapiro provided helpful comments. Perwinder Singh provided excellent research assistance. Contact: eoster@uchicago.edu. 1

2 television viewership in the Muslim world affects attitudes towards the West, and DellaVigna and Kaplan (2007) show large effects of the Fox News channel on voting patterns in the United States. In the developing world, Olken (2006) shows that television decreases participation in social organizations in Indonesia, and Chong, Duryea and La Ferrara (2007) find that exposure to soap operas in Brazil reduces fertility. India has not been left out of the cable and satellite revolution: a recent survey finds that 112 million households in India own a television, with 61 percent of those homes having cable or satellite service (National Readership Studies Council 2006). This figure represents a doubling in cable access in just five years from a previous survey. The survey finds that in some states, the change has been even more dramatic; in the span of just years since it first became available, cable or satellite penetration has reached an astonishing 60 percent in states such as Tamil Nadu, even though the average income is below the World Bank poverty line of two dollars per person per day. Beyond providing entertainment, television vastly increases both the availability of information about the outside world and exposure to other ways of life. This is especially true for remote, rural villages, where several ethnographic and anthropological studies have suggested that television is the primary channel through which households get information about life outside their village (Mankekar 1993, 1998; Fernandes 2000; Johnson 2001; Scrase 2002). Most popular cable programming features urban settings where lifestyles differ in prominent and salient ways from those in rural areas. For example, many characters on popular soap operas have more education, marry later and have smaller families, all things rarely found in rural areas; and many female characters work outside the home, sometimes as professionals, running businesses or in other positions of authority. Anthropological accounts suggest that the growth of TV in rural areas has had large effects on a wide range of day-to-day lifestyle behaviors, including latrine building and fan usage (Johnson 2001). Yet there have been few rigorous empirical studies of the impacts this dramatic expansion in cable access may have had on social and demographic outcomes. In this paper we explore the effect of the introduction of cable television in rural areas of India on a particular set of values and behaviors, namely attitudes towards and discrimination against women. Although issues of gender equality are important throughout the world, they are particularly salient in India. Sen (1992) argued that there were 41 million missing women in India women and girls who died prematurely due to mistreatment resulting in a dramatically male-biased population. The population bias towards men has only gotten worse in the last two 2

3 decades, as sex-selective abortion has become more widely used to avoid female births (Jha et al. 2006). More broadly, girls in India are discriminated against in nutrition, medical care, vaccination and education (Basu 1989; Griffiths et al. 2002; Pande 2003; Borooah 2004; Mishra et al. 2004; Oster 2007). Even within India, gender inequality is significantly worse in rural than urban areas. By exposing rural households to urban attitudes and values, cable and satellite television may lead to improvements in status for rural women. It is this possibility that we explore in this paper. The primary analysis relies on a three-year panel dataset covering women in five Indian states between 2001 and These years represent a time of rapid growth in rural cable access. During the panel, cable television was newly introduced in 21 of the 180 sample villages. 1 Our empirical strategy relies on comparing changes in gender attitudes and behaviors between survey rounds across villages based on whether (and when) they added cable television. Using these data, we find that cable television has large effects on women s status. After cable is introduced to a village, there are significant changes in gender attitudes: women are less likely to report that it is acceptable for a husband to beat his wife, and less likely to express a preference for sons. Behaviors traditionally associated with women s status also change: women report increased autonomy (for example, the ability to go out without permission and to participate in household decision-making) and lower fertility. In terms of magnitude, the effects are quite large for example, the introduction of cable decreases the differences in attitudes and behaviors between urban and rural areas by 45 to 70 percent. Further, these effects happen quickly, with observable impacts in the first year following cable introduction. This is consistent with existing work on the effects of media exposure, which typically find rapid changes (within a few months, in many cases) in behaviors like contraceptive use, pregnancy, latrine building and perception of own-village status (Pace 1993; Valente et al. 1994; Kane et al. 1998; Rogers et al. 1999; Johnson 2001). A central empirical concern is the possibility that trends in other variables (for example, income or modernity ) affect both cable access and women s status. We argue that this does not appear to be the case, first showing visually that there are no pre-existing differential trends in women s status for villages that do and do not add cable, and that the timing of changes in outcomes is closely aligned with the introduction of cable, and second, that the outcomes are not correlated with future cable access. 1 Cable television in these villages is generally introduced by an entrepreneur, who purchases a satellite dish and subscription, and then charges people (generally within 1km of the dish) to run cables to it. In this sense, people are actually accessing satellite channels. We will use the terms cable and satellite interchangeably to refer to programming not available via public broadcast signals. Our interest is with the content of programming available to households, rather than the physical means of delivery of that content. 3

4 Policy makers and academics often argue that a significant benefit of improved status for women is increased investments in children (World Bank 2001, 2006; Qian 2008). Although our ability to look at children s outcomes is limited, we are able to look at the effects of cable access on school enrollment. Using both our household panel data and administrative data for roughly 1,000 villages in the state of Tamil Nadu, we provide evidence that the introduction of cable increases school enrollment for younger children. Although the enrollment data have some limitations relative to the data on women s status, we see large effects of cable that also appear to increase over time. Again, we argue these results are not driven by pre-existing trends in the outcome variables. The results are potentially quite important for policy. As noted, a large literature in economics, sociology and anthropology has explored the underlying causes of discrimination against women in India, highlighting the dowry system, low levels of female education, and other socioeconomic factors as central factors (Rosenzweig and Shultz 1982; Murthi et al. 1995; Agnihotri 2000; Agnihotri et al. 2002; Rahman and Rao 2004; Qian 2008). And while progress has been made in these areas, changing the underlying factors behind low levels of education, women s status and high fertility has proven to be very difficult; introducing television, or reducing any barriers to its spread, may be less so. In fact, the government of Tamil Nadu has recently begun a program to provide free color televisions to 7.5 million households with the goal of ensuring every household has one by One of the primary objectives of this program is to enable women, particularly in rural areas, to acquire knowledge for social and economic development. Therefore, our results also provide insight into the potential impact this unique and non-traditional strategy can have on critical policy priorities. From a policy perspective, however, there are potential concerns about whether the changes in reported attitudes, such as towards domestic violence or son preference, represent changes in behaviors, or just in reporting. For example, we may be concerned that exposure to television only changes what the respondent thinks the interviewer wants to hear about the acceptability of beating, but does not actually change the incidence of beating. This is less of a concern in the case of autonomy and fertility, where women are asked about their actual behavior (and for fertility, there is less scope for misreporting since both pregnancies and recent births are likely to be observable by the interviewer). In addition, the fact that we find effects on education in administrative data provides support for an effect of cable on behavior. Without directly observing people in their homes, however, it is difficult to conclusively separate changes in reporting from changes in behavior. However, even if cable only changes what is reported, it still may represent progress: changing the 4

5 perceived correct attitude seems like a necessary, if not sufficient, step toward changing outcomes. The remainder of the paper is organized as follows. Section 2 provides background on television in India and discusses existing anthropological and ethnographic evidence on the impact of television on Indian society, as well as the determinants of cable placement. Section 3 describes the SARI data and empirical strategy. Section 4 presents the results on women s status, and Section 5 the results on education. Section 6 provides some discussion of magnitudes, timing and mechanisms behind the results and concludes. 2 Background on Television in India While television was first introduced to India in 1959, for the first three decades almost all broadcasting was in the hands of the state, and the content was primarily focused towards news or information about economic development. 2 The most significant innovation in terms of both content and viewership was the introduction of satellite television in the early 1990s. In the 5 years from 2001 to 2006 about 30 million households, representing approximately 150 million individuals, added cable service (National Readership Studies Council 2006). And since television is often watched with family and friends by those without a television or cable, the growth in actual access or exposure to cable is likely to have been even more dramatic. The program offerings on cable television are quite different than government programming. The most popular shows tend to be game shows and soap operas. For example, among the most popular shows in both 2000 and 2007 (based on Indian Nielsen ratings) is Kyunki Saas Bhi Kabhi Bahu Thi, (Because a Mother-in-Law was Once a Daughter-in-Law, Also) a show based around the life of a wealthy industrial family in the large city of Mumbai. As can be seen from the title, the main themes and plots of the show revolve around issues of family and gender. The introduction of television appears in general to have had large effects on Indian society. This is particularly the case for gender, since this is an area where the lives of rural viewers differ greatly from those depicted on most popular shows. By virtue of the fact that the most popular Indian serials take place in urban settings, women depicted on these shows are typically much more emancipated than rural women. Further, in many cases there is access to Western television, where these behaviors differ even more markedly from rural India. Based on anthropological reports, this seems to have affected attitudes within India. Scrase (2002) reports that several of his respondents 2 The background information detailed here is drawn largely from Mankekar (1999) and 5

6 thought television might lead women to question their social position and might help the cause of female advancement. Another woman reports that, because of television, men and women are able to open up a lot more (Scrase 2002). Johnson (2001) quotes a number of respondents describing changes in gender roles as a result of television. One man notes, Since TV has come to our village, women are doing less work than before. They only want to watch TV. So we [men] have to do more work. Many times I help my wife clean the house. There is also a broader literature on the effects of television exposure on social and demographic outcomes in other countries. Many studies find effects on a variety of outcomes: for example, eating disorders in Fiji (Becker 2004), sex role stereotypes in Minnesota (Morgan and Rothschild 1983) and perceptions of women s rights in Chicago (Holbert, Shah and Kwak 2003). Telenovelas in Brazil have provided a fruitful context for studying the effects of television. For example, based on ethnographic research, La Pastina (2004) argues that exposure to telenovelas provides women (in particular) with alternative models of what role they might play in society. Pace (1993) describes the effect of television introduction in Brazil on a small, isolated, Amazon community, arguing that the introduction of television changed the framework of social interactions, increased general world knowledge and changed people s perceptions about the status of their village in the wider world. Kottak (1990) reports on similar data from isolated areas in Brazil, and argues that the introduction of television affects (among other things) views on gender, moving individuals in these areas towards having more liberal views on the role of women in both the workplace and in relationships. And closely related to one of our outcomes, Chong, Duryea and La Ferrara (2007) report declines in fertility in Brazil in response to access to telenovelas; they also find changes in naming patterns of children, with the names of main characters featured on these programs increasing in popularity. Interestingly, the ethnographic and anthropological studies in Brazil also suggest that the patterns of viewing shortly after television is first introduced may be quite different than what is seen later on. The evidence suggests that in the first years after introduction, interactions with the television are more intense, with the television drawing more focus (both at an individual level, and community-wide). It is during this early period that Kottak (1990) and others argue that television is at its most influential. Most of the villages in our analysis are at this early stage of television exposure, suggesting this may be an ideal period to look for effects. With the exception of Chong, Duryea and La Ferrara (2007), much of the evidence described above is drawn from interviews and case studies, and obviously does not reflect a random 6

7 sample of these populations. Nevertheless, the overall impression given by the anthropology and sociology literature is that the introduction of television has widespread effects on society, and that gender and social issues are a particular focal point. Our data and setting provide an opportunity to test this hypothesis more rigorously. The Placement and Timing of Cable Access In moving to a more quantitative analysis of the impacts of cable, we must recognize that variation in access is certainly non-random. Therefore, understanding the determinants of the timing and placement of cable is important for our ability to attribute changes in women s status to the introduction of cable itself. To determine what drives the introduction of cable, we first conducted interviews with cable operators in Tamil Nadu. In these interviews, the operators emphasized two primary considerations: access to electricity and distance to the nearest town or city. Electricity is, of course, a fundamental requirement for television. Distance is important because most operators who provide service to rural villages reside in towns or cities. Greater distances (i.e. more remote villages) increase the operator s costs, since they often must personally travel to the village to monitor the cable set-up (to ensure it is working properly and that no unauthorized users are connecting to it), collect payments, make repairs or update equipment, or add new subscribers. 3 For the most remote villages, a single trip could require an entire day. As a result, villages closest to larger towns were served first, with more distant villages only being covered after the more profitable villages were taken. Income was less often mentioned by operators as a constraint, since charges for cable access are small (about $1-2 U.S. per month); though in separate interviews with companies marketing televisions, this was more of a concern. Overall, most cable operators reported that variation in access was driven largely by costs, and changes in costs, on the part of the providers themselves, rather than being demand-driven. In fact, several stated that they believed demand was universal (at least in Tamil Nadu), and the only constraint on provision was the operator s costs. In addition to these interviews, we also conducted a survey of cable operators in Tamil Nadu, gathering information on cable access for over 1,000 rural villages in Tamil Nadu (these data are described in more detail in section 5). For 220 of villages in our survey that do not have cable as of 2008, surveyors recorded the reason(s) given for lack of access. For a majority of the villages 3 Outside of major cities, many of these operators are fairly small businesses, serving from a handful to a dozen or so villages, so much of this work is done by the individual entrepreneurs themselves. 7

8 (62%) the main reason was that the village was too far away; the other major reason (30%) was that the village was too small to support cable. The cable operator data can be used to examine the determinants of cable access more quantitatively by merging villages with administrative data from an education database (again, described in more detail in Section 5). Doing so allows us to examine the village-level relationship between cable access and the correlates suggested above: distance to a town, population, and electrification. 4 Panel A of Table I shows bivariate correlations between these variables and having cable in 2008 (column (1)) and year of cable introduction among villages that have cable in 2008 (column (2)). Consistent with the reports from cable operators, villages that are further from a town get cable later and are overall less likely to have cable in 2008, while having electricity or a larger population has the opposite effects. We see similar patterns in the multivariate regressions in Panel B. We can also use information from our other data source, the Survey of Aging in Rural India, to do a similar analysis of the village-level determinants of having cable at the time of the baseline survey in 2001; the sample size is smaller, but the survey has more variables and was conducted in five states. Column (3) in Panel A of Table I reports bivariate correlations between cable access and electricity, distance to the nearest town, 5 population density, and average income and education. The results are similar to those from the Tamil Nadu data. Panel B shows multivariate regressions with these variables and, in column (4), includes state fixed effects to capture the fact that access varies significantly across states. The broad patterns remain the same. In these data, there is some evidence of a role for income as a determinant of cable access, suggesting it may be important to control for it even though it was not explicitly mentioned by cable operators. These quantitative results are supportive of the qualitative evidence from the interviews and suggest the importance of controlling for these determinants of cable access in our analysis. Under the assumption that these variables constitute the primary determinants of access, controlling for them should allow us to more convincingly attribute the changes in the outcomes to the introduction of cable. It is important to note, however, that the R-squared values in the regressions in Table I are small, indicating that much of the variation in cable access remains unexplained. One possibility, of course, is that other than these important variables, entrepreneurs choose where and 4 Electricity in the village is inferred from information about electricity in the schools and population is only for children aged The SARI survey did not define town for the purposes of measuring distance, so there is likely to be some variation across villages in what this variable measures. 8

9 when to introduce cable somewhat arbitrarily, or at least based on factors that are unlikely to have an independent effect on women s status. 6 But we certainly cannot rule out that there is some important variable that drives cable introduction that was not mentioned by cable operators and that also has an impact on our outcomes of interest. Given this, it is important to look directly at whether the introduction of cable is predictable from the levels of, or changes in, our dependent variables. Web Appendix Table W.1 shows that, conditional on the simple controls used above, adding cable during the sample period does not appear to be systematically related to initial levels of our measures of women s status (discussed in more detail in the next section). 7 In most cases, the coefficient on adding cable is close to zero. In fact, villages that later add cable are initially slightly more inclined to report domestic violence is acceptable and have higher initial levels of fertility. In addition to these results, we will show evidence later that getting cable in future years is not predictive of changes in outcomes. However, what we cannot rule out with our data is that there is some important unobservable which was not mentioned by cable operators that simultaneously drives year-to-year cable introduction and year-to-year variation in our outcome measures; although this seems unlikely, and we are unable to think of plausible examples, it is important to keep this caveat in mind. 3 Data and Empirical Strategy 3.1 Data: Survey of Aging in Rural India Our primary dataset is the Survey of Aging in Rural India (SARI), a panel survey of 2,700 households, each containing a person aged 50 or older, conducted in 2001, 2002 and 2003 in four states (Bihar, Goa, Haryana and Tamil Nadu), and the capital, Delhi. The sample was selected in two stages: in the first stage, 180 villages were selected at random from district lists (40 villages in Bihar, Haryana and Tamil Nadu, 35 in Delhi and 25 in Goa), and in the second stage, 15 households were chosen within each village through random sampling based on registration lists. Other than Delhi, the survey was confined to rural areas. Attrition over the panel was low, with just 108 (4 percent) of the original households dropping out by the third round. All women in the sample households aged 15 and older were interviewed (no men were 6 And indeed, this possibility is consistent with some of the reports of cable operators in our interviews. For example, some operators said they had chosen particular villages because they were the home villages of the person who cleaned their office or the woman in the market they bought vegetables from. 7 Web Appendix Table W.1 appears in Web Appendix W, which is accessible on the authors websites as well as on the Journal s website. All Appendix Tables can be found in this supplemental appendix. 9

10 interviewed). Several sections of this survey were modeled to be compatible with other demographic surveys for India, such as the National Family and Health Survey (NFHS). The survey collected information on a range of (current and past) demographic, social and economic variables. In addition, a village-level survey with local government officials gathered information on economic and social conditions and infrastructure. Basic summary statistics on the women included in the SARI sample are provided in Panel A of Table II. Women in the sample have relatively little education (an average of 3.5 years), are predominantly Hindu, and are quite poor, with an average per capita income of around $35 per month. The SARI data contain a number of measures of women s status. We begin with two attitude measures: son preference and the acceptability of domestic violence. For the former, women who reported wanting to have more children were asked: Would you like your next child to be a boy, a girl or it doesn t matter? Son preference is defined as wanting the next child to be a boy. For domestic violence, women were asked: Please tell me if you think that a husband is justified in beating his wife in each of the following situations: If he suspects her of being unfaithful; if her natal family does not give expected money, jewelry or other things; if she shows disrespect for him; if she leaves the home without telling him; if she neglects the children; if she doesn t cook food properly. The outcome measure we use is the number of situations in which the woman reports that beating is acceptable (0-6). Summary statistics for these two measures are in Panel B of Table II. Over 60 percent of women feel that it is acceptable for a husband to beat his wife under at least one of the six situations listed. On average, women report 1.6 situations in which it is considered acceptable. Women are most likely to believe beating is acceptable if a wife neglects her children, goes out without permission or does not show respect towards her husband. Perhaps surprisingly, being unfaithful is reported as valid justification for violence by slightly fewer women. In terms of son preference, 55 percent of women who want another child prefer that child to be a boy. 8 The residual is not simply preferring a girl; only about 13 percent of women want their next child to be a girl, with the remainder reporting that the sex of the child doesn t matter (about one-quarter of the sample) or reporting something else (such as up to God ). In addition, we analyze the effects on two behaviors associated with women s status: household decision making (autonomy) and fertility. To measure autonomy, women were asked (separately for each of the following activities), Who makes the following decisions in your 8 Note, the sample size for this variable is smaller because the question is only asked to women who want more children. 10

11 household: Obtaining health care for yourself; purchasing major household items; whether you visit or stay with family members or friends? The possible responses were: 1. Respondent; 2. Husband; 3. Respondent jointly with husband; 4. Other household members; 5. Respondent jointly with other household members. Women were also asked whether they need permission from their husbands to visit the market (one question) or to visit friends or relatives (a second question). Responses were coded on a scale of 1 to 3 (do not need permission; need permission; not permitted at all). Women were also asked whether they are allowed to keep money set aside to spend as they wish. Finally, fertility is measured by asking female respondents if they are currently pregnant. Later, we will also use birth histories to construct earlier trends in fertility. Panel B of Table II reports summary statistics for these measures. For the decision-making variables, we condense the responses to binary indicators for whether the woman participates in the decision (either decides on her own or decides jointly with others in the household). Overall, slightly more than half of women participate in each of the decisions. There is some overlap in these variables, though not as much as might be suggested by the similarity of their means; about 20 percent of women do not participate in any of the decisions, 25 percent participate in one, 27 percent in two, and 29 percent in three. About one-half of women report needing permission to go to the market and two-thirds need permission to visit family or friends. 9 By contrast, nearly three-quarters of women are allowed to keep money set aside to spend as they wish. However, by most measures, women s autonomy overall is quite low. For our empirical analysis, we redefine the variables so higher values always indicate greater autonomy, and average the six variables to generate a single measure ranging from zero to one. Data on cable access in SARI is based on information collected in a village questionnaire that gathered information on a variety of services and infrastructure. Thus cable is measured at the village level, not the individual level. Panel A of Table III provides information on cable access throughout the survey period, which, again, was a time of rapid expansion in access. In our data, 90 of the 180 villages have cable in the first round, while an additional 11 villages added cable by the 2002 survey and another 10 added it by Finally, 69 villages never get cable during this period (no villages dropped cable). The identification of the effects of cable in this paper relies on the 21 villages that added it in either 2002 or There is significant regional variation in access to cable in the SARI data. Panel B of Table III shows the percent of sample villages in each state with access in Not surprisingly, the 9 While not shown here, about 4 percent are not permitted to do each of these things. 11

12 capital, Delhi, has essentially universal access. Elsewhere, the two southern states of Tamil Nadu and Goa have very high cable penetration, at 78 and 60 percent of villages, respectively. By contrast, coverage in the two northern states of Bihar and Haryana is low (7 and 17 percent). While this variation may seem extreme, or perhaps an artifact of our particular sample of rural villages, these estimates are consistent with a 2001 national census of villages (NSSO 2003). 3.2 Empirical Strategy Our basic empirical strategy is to compare changes in our measures of women s status for villages that add cable over the course of the panel relative to those that do not. We run individual-level fixed effects regressions of each outcome on cable availability (measured at the village-level). Denote the outcome for individual i in village v in year t as s ivt and the measure of cable access as c vt. The primary regression estimated is s ivt = βc vt + γ iv + δ t + τx ivt + ɛ ivt (1) where γ iv is a full set of individual fixed effects, δ t is a full set of year dummies and the other controls, X ivt, include household income, and a quadratic in age. Our identifying assumption is that villages that added cable would not have otherwise changed differently than those villages that did not add cable. We discuss this in more detail below. While any fixed village characteristics that determine cable access will be absorbed by the fixed effects, in order to account for any possible differential trends in the outcomes by these factors, we also include in the regressions interactions between a year indicator and state dummies, income, education, age and age-squared, village population density, electrification status and distance to nearest town. Data for all these variables except income was collected only at baseline, and thus we are only controlling for differential trends based on initial values of these variables, not for actual trends in these variables. Standard errors are adjusted for clustering at the village level. 4 Results: Cable and Women s Status Before turning to the effect of cable on outcomes, it is worth briefly exploring the effect of cable access on TV watching. Since all villages have long had access to broadcast television, it is ex ante unclear whether the introduction of cable will change the amount of television watched, which is potentially important as a first stage (although cable is likely to change the content of TV 12

13 watched, even if it does not change the amount, which could also have an effect). We can use the SARI data to provide some information on this question. Figure I shows the percent of women who report they watch television at least once a week (unfortunately, the survey did not gather data on the amount of time spent watching). The graph shows viewership for each year for women in four groups of villages: those that already have cable as of 2001, those that add cable in 2002, those that add cable in 2003, and those that never get cable during our survey. Overall, there is relatively little change in watching over time in either areas that never have cable or those that always have it. However, in villages that get cable in 2002, the share of respondents who report watching television at least once a week jumps from 40 to 80 percent between 2001 and 2002; in villages that get cable in 2003, this share is constant between 2001 and 2002, then increases sharply from 50 to 90 percent between 2002 and This graph suggests a strong connection between cable availability and television viewership, with a near doubling in both cases. Having established that cable increases TV watching, we turn to the effects on women s status. Figures II-V mimic the format of Figure I and show the effects of cable access on attitudes towards spousal abuse and son preference, autonomy and fertility. We begin with attitudes because these are, perhaps, the most likely place to see an effect of cable. Changing behaviors (autonomy, for example) may require coordination with other family members and larger scale changes in lifestyle. Changing attitudes, which may or may not be accompanied by changes in behavior, is the first and most obvious place to look for effects of exposure to other lifestyles and values. Figure II focuses on attitudes towards beating. The number of situations in which beating is reported to be acceptable by women is relatively unchanged in the villages that don t change cable status (either those who always have cable or never get it). However, there is a large decrease in the average number of situations in which it is considered acceptable between 2001 and 2002 for those that get cable in 2002, and a (somewhat smaller) decrease between 2002 and 2003 for those that get cable in Figure III shows the same pattern for son preference: reported desire for the next child to be a son is relatively unchanged in areas with no change in cable status, but it decreases sharply between 2001 and 2002 for villages that get cable in 2002, and between 2002 and 2003 (but 10 The levels in this graph may appear to contradict the finding that cable reduces the acceptability of beating, as villages that always have cable report on average much higher acceptability that those that never get it. However, this effect is driven largely by villages in Tamil Nadu, which have very high average reported acceptability of beating and high cable access. Adding state fixed effects to a regression of acceptability of beating on cable access eliminates, and in fact reverses, the apparent contradiction. Note that since our empirical analysis focuses on changes rather than levels, this effect will similarly be eliminated. It is worth noting, also, that this issue does not arise with the other outcomes, where the levels are more consistent with the changes. The outlier status of Tamil Nadu, with very high levels of acceptability of beating, but higher levels of women s status in other dimensions, is consistent with patterns observed elsewhere (IIPS and ORC Macro, 2000). 13

14 notably not between 2001 and 2002) for those that get cable in For both measures of attitudes, the changes are large and striking, and correspond closely to the timing of introduction of cable. Next, we turn to measures that are reflective of behaviors, namely women s reports of their actual autonomy and fertility. Figure IV shows the effects of cable on female autonomy. Again, as in Figures II and III, we see no change in autonomy for areas with no changes in cable, but large increases in autonomy in villages that add cable, and these changes again coincide closely with the timing of cable introduction. Finally, Figure V shows effects on fertility. Although the fertility data is noisier since the sample sizes are smaller, we see roughly the same pattern. There is no particular trend for areas that always have or never get cable, but for areas that get cable in 2002 there is a decrease in fertility between 2001 and 2002, and for those that get cable in 2003 there is a decrease between 2002 and Panel A of Table IV turns to results from fixed effects regressions of the form in Equation (1). 11 Columns (1) and (2) show the attitude results, which are consistent with the previous graphs. Adding cable is associated with a 12 percentage point decrease in the reported preference to have the next child be a boy, and a 0.16 decrease in the number of situations in which it is considered acceptable for a man to beat his wife (relative to a base of 1.61). Column (3) shows the autonomy effects; again, consistent with Figure IV, the effect of cable is positive and statistically significant, improving the autonomy index by 0.026, from a base of about This table also reports means and standard deviations of the outcome variables, to give a sense of the magnitude of the results. Column (4) shows the effect of cable on reported pregnancy during the sample period. Getting cable leads to approximately a 3.7 percentage point decrease in the likelihood of pregnancy. This effect is extremely large. However, with our data, we are unable to determine whether the reduction in current pregnancies reflects a decline in the total number of births a woman will have, or simply increased spacing of births (though even the latter would reflect gains for women, and potentially, their children). As mentioned above, the most significant issue facing the basic difference-in-difference results presented above is the possibility that some unobserved variable for example, attitudes towards modernity is driving both the introduction of cable and changes in women s status. Since variables such as this are likely to change gradually over time rather than suddenly or all at once, we might expect these effects to be evident in the form of pre-existing trends in the data. That 11 Web Appendix Table W.2. shows these regressions without the individual fixed effects for the interested reader, in specifications both excluding and including village fixed effects. The regressions with village fixed effects look very similar to those with individual fixed effects; the regressions without either set of fixed effects look quite different. 14

15 is, we would see changes in the outcomes of interest anticipating changes in cable access, since an outside variable would be driving both. We can look for evidence of this possibility first in Figures II-V. In particular, although we have a limited panel, and therefore limited scope for this test, we can look at whether villages that got cable in 2003 show evidence of changes in women s status between 2001 and In no case do we see evidence for this women s status in these villages is largely unchanged between 2001 and 2002 across all measures, and then changes sharply between 2002 and The close correspondence between the introduction of cable and sudden changes in the measures of women s status suggests a direct link between the two, rather than pre-existing trends in the outcomes. We can test statistically for the possibility of pre-trends more formally by including in the regressions above an indicator for getting cable next year. This coefficient is identified off of the changes between 2001 and 2002 for the 10 villages that add cable in 2003, plus changes between 2002 and 2003 for four villages that were recorded to have gotten cable within three months of the 2003 survey. 12 The results in columns (1)-(4) of Panel B of Table IV indicate that the effect of getting cable this year is largely unchanged, compared to Panel A and more importantly that getting cable one year later does not predict changes in women s status. In other words, it does not seem that there are changes in the outcomes that anticipate getting cable. It is important to note that not only are these coefficients not statistically significant, but the point estimates are extremely small, and in most cases statistically significantly different from the effect of getting cable this year so we can reject that any changes observed about after cable is introduced are simply the continuation of pre-existing trends. In addition to this evidence, for fertility (only) we can use birth histories to examine for pre-existing trends over a longer period. Women in the survey were asked to report on each of the children they had given birth to. We can use these data to construct measures of whether they were pregnant in previous years, during the same survey month. For example, for women interviewed in June of 2001, we can determine whether the woman was pregnant in June 2000 based on whether she reported giving birth any time between June 2000 and March We can similarly construct measures of pregnancy going back several years (we go back only until 1997). 13 This measure will of 12 Our survey team returned to all sample villages three months after the 2003 survey in order to gather some additional data, which allowed us to identify four villages that had added cable after the survey. One question with these regressions is how to treat villages that do not have cable in Some of these village might have gotten cable between our return survey and 2004, although most of them will not have. In Panel B of Table IV we include these villages and assume they did not get cable, which preserves the sample sizes and comparability to Panel A. However, we have run these analyses excluding these villages and the results are very similar (available from the authors). 13 While it is be possible to go back further, evidence from validation studies of fertility histories finds that there is 15

16 upward. 14 In column (5) of Table IV we use this longer, constructed panel of fertility to control for course be imperfect; for example, women in the early stages of pregnancy may not know or may not report they are pregnant, leading to lower apparent pregnancy rates when measured by directly asking about current pregnancies. On the other hand, since many pregnancies result in spontaneous or induced abortions rather than births, estimating pre-survey pregnancies by counting backwards from pre-survey births will lead to an undercount. While these measurement difficulties make our estimated effects of cable on pregnancies less precise, they should not lead to bias since they should not be systematically correlated with whether the village has cable. But for the sake of consistency, when we generate the pre-survey pregnancy measures, we adjust the reported pregnancy levels village-specific fertility trends. While this approach adds additional years of data, we have to exclude any villages that have cable in 2001, since we do not observe when they get cable. Even with the inclusion of the village-specific fertility trends, the coefficient on cable is negative and significant, indicating that the addition of cable produces a net-of-trend drop in pregnancy. 15 In fact, the point estimate is increased by the inclusion of these trends, suggesting that relative to villages that did not add cable, those adding cable were if anything actually experiencing increasing trends in fertility prior to the introduction of cable (though we would not reject that the coefficients in the two regressions are equal). Finally, column (5) of Panel B again shows no effect of future cable. Overall, the results in Table IV and Figures II-V suggest that the introduction of cable in these rural villages led to an improvement in the status of women, visible both in changes in attitudes and behaviors. Although improving women s status is an important goal in and of itself, several authors have argued that human capital investments in children are greater when women have higher status within the household (see, for example, Qian, 2008; World Bank, 2001; World Bank, 2006). We therefore turn now to looking at one specific outcome, education. 16 underreporting of births, especially when the child later dies, for births more than five years in the past, but little such underreporting of births in the previous five years (Bairagi et al. 1997). 14 We do this by comparing reported pregnancies in the 2001 survey to estimated pregnancies based on reported births in the 2002 births history. The adjustment factor is 1.43, suggesting women start reporting pregnancy when they are around 3 months pregnant, which seems realistic. It is worth noting that this adjustment addresses the possibility of both undercount and overcount by simply estimating the factor that makes the two measures comparable. 15 As noted in the data section, only 21 villages in the SARI data change their cable status during the sample. This could lead to the concern that the results presented here are driven by outliers; however, we have run all of the SARI regressions leaving out each changer village in turn, with no noticeable effect on the results (results available from the authors). 16 It is worth noting, of course, that the effects of cable on education may be interesting in their own right, regardless of the link with women s status. 16

17 5 Results: Cable and Education Our analysis of schooling makes use of two datasets: the SARI data, and administrative data on school enrollment from Tamil Nadu. SARI Data Analysis The household roster file in the SARI survey records whether each child in the household is enrolled in school. We aggregate these data to the village level, and analyze the effect of cable access on school enrollment via three distinct groups. First, we follow enrollment among a fixed cohort of girls and boys aged 6-7 in the first survey year (2001) as they progress through school over the course of the panel. Second, we examine enrollment by sex for children aged 6-10 in the given survey year (i.e., this does not follow the same students over time; students aged 5 in 2001 enter the group when they turn 6 in 2002 and students aged 10 in 2001 exit the group in 2002 when they turn 11). Finally, we similarly examine enrollment by sex for children aged in the given survey year. Summary statistics for these variables are in Panel C of Table II. Echoing the format of previous figures, Figures IVa and IVb illustrate the basic results using enrollment for children aged Focusing first on girls, although the results are noisier than for women s status, we do see evidence for an impact of cable on school enrollment. In the villages that do not change cable status, enrollment is either flat or slightly decreasing over the sample period. For those that get cable in 2002, enrollment increases between 2001 and 2002, and then further increases between 2002 and 2003, suggesting some increasing impact over time. For those that get cable in 2003, enrollment decreases from 2001 to 2002 (consistent with the change in villages that always have cable) but increases from 2002 to Figure IVb, for boys, shows less clear results and, in particular, does not appear to demonstrate any on-introduction impact of cable: moving from 2001 to 2002 in the 2002 adopters or 2002 to 2003 in the 2003 adopters does not appear to increase enrollment. However, there is a large increase between 2002 and 2003 for the 2002 adopters, again perhaps pointing to a delayed effect. Although the results for women s status suggests immediate on-introduction effects, it is possible that any effects on education could take longer, for example because plans for education must be made further in advance and money must be saved for fees and other costs. Table V turns to the regression results. The format of regressions is the same as in the analysis of women s status, although the controls are village-level averages and village fixed-effects 17

18 replace individual fixed-effects. 17 Panel A presents the basic results. In columns (1) and (2), which focus on the fixed cohort of children aged 6-7 in 2001, the coefficients for both groups is positive, though neither is statistically significant. Columns (3) and (4) focus on children aged 6-10, and reveal a large (12 percentage point) and statistically significant increase in enrollment for girls, but no effect for boys. Columns (5) and (6) look at older children, where there is no effect for either sex. Panel B explores whether there is evidence of a delayed effect of cable on school enrollment, as was suggested by Figures IVa and IVb. The regressions include an indicator for having cable for one year, and having cable for two years. 18 The sample excludes the 2001 data for villages that have cable in 2001 since we do not know how long they have had cable; by 2002 we know they must have had cable for at least 2 years, so we include the data for 2002 and The evidence in Panel B does show some evidence of a delayed effect the coefficients on having cable for two years are typically larger than for having cable for one year. Though again the results are only statistically significant for younger girls. The fact that we don t find effects for older children may reflect the higher costs of sending older children to school (both direct outlays and opportunity cost), or that many older children have already left school, and it is difficult to return after having dropped out. In Panel C of Table V we address the issue of possible pre-trends as we did above, by including an indicator for getting cable in the next year. As above, the inclusion of this variable does not markedly change the effects of cable access and we see no evidence that changes in enrollment anticipate cable introduction. DISE Data Analysis We provide additional evidence on the impact of cable on education using a second dataset of administrative data on schools from Tamil Nadu compiled as part of the national District Information System for Education (DISE). 19 The records contain data on enrollment and school characteristics for each school in Tamil Nadu yearly for the through school years. Using these data, we construct enrollment measures that parallel the SARI data. We first follow enrollment by sex for a fixed age cohort who are 6-7 years old in the school year. 20 In 17 Analysis using individual-level enrollment data, reported in Appendix Table W.3, yield similar results. 18 Though since the variable for having cable for two years can only be one for villages that add cable in 2002, this result could also reflect in part a larger impact for those villages relative to those that add cable in 2003, instead of/or in addition to any increasing impact of cable. 19 These data were obtained on CD from the Tamil Nadu district education office in Chennai. 20 Note that this close to what we do in SARI but not identical; in the SARI data we can actually follow the same children over time. In the DISE data, we cannot be sure if the 7-8 year old children in 2003 are the same as the 6-7 year old children in In this sense we are following a cohort of fixed age, but not fixed individuals. 18

19 addition, we separately analyze enrollment by sex of children aged 6-10 and in each year. Summary statistics for these variables are in Panel C of Table II. To match the DISE data, we gathered information on cable access for 1,061 villages in Tamil Nadu in March and April of To select the villages, we identified five districts that had low cable penetration in 1998 (the latest year for which we have data from a separate survey, the 1998 National Family Health Survey, that allow us to identify district-level cable penetration). We chose low access districts because our empirical analysis relies on comparing villages that added cable to those that did not, and districts with high cable penetration rates in 1998 would have had few villages adding cable after 2002 (the first year of the DISE data). Within these five districts (Salem, Tiruvannamalai, Pudokkottai, Sivaganga and Ramanathapuram) we randomly selected a total of 19 blocks (administrative units comprising groups of villages) and gathered information on all villages within those blocks. Surveyors were sent out to block headquarters to locate cable operators. For each village in the block, the surveyor asked the operators whether the village had cable, and in what year they got it. Approximately 63% of the villages in the sample had cable at the time of the survey, with starting dates ranging from 1989 through For the 2002 to 2007 period covered by the administrative education data, 394 villages already had cable in 2002, 277 added cable during the period and 390 never got cable. Panel C of Table III show the number of villages receiving cable for each year. 21 These data show that the period covered by the DISE data represents a time of significant cable expansion in these blocks. 22 The DISE data have several advantages relative to the SARI data, including providing objective, rather than self-reported, enrollment data, and a longer time-series (valuable for checking for pre-trends) with more variation in the timing of access. A significant limitation of these data, however, is that records provide only raw enrollment numbers, and for most villages we only have child population data for one year of the survey (2005). As a result, we can only analyze total enrollment, not enrollment rates. Finding an effect of cable access on enrollment in these data is 21 A number of villages are recorded as having had cable at some point but then losing it. Most such cases involved switching from traditional cable access (an entrepreneur with a satellite dish sells cable connections) to direct-to-home (DTH) satellite service. We code these villages as having cable, since DTH provides access to the same programming available through cable. However, 28 villages are recorded as having lost cable for some other reason (such as failure to pay or problems with local partners). In these cases we know the beginning and ending date of cable access and code the access accordingly. Dropping year*village observations after service is dropped to allow for the possibility of persistent or delayed effects of cable does not change the results appreciably. 22 Table 3 shows some evidence of bunching of cable access at 2000 and This may be due to misreporting or rounding in the reports by the cable operators. We have run the analysis excluding villages getting cable in 2000 or 2005, and find no significant differences (results available from the authors). 19

20 therefore consistent either with an increase in enrollment rates or an increase in total school-age population with no changes in the likelihood of enrollment. This is not an issue in the SARI data where we have a fixed population over time. Although with these data we are unable to fully address this issue, below we provide two pieces of evidence that, while only suggestive, do point to an enrollment effect rather than population growth. One other challenge is that our village survey only reported the year the village got cable, not the month. This makes it difficult to match with precision the timing of cable introduction and school enrollment. For example, villages that received cable in 2002 may have added it in August, when it was already too late to influence the decision on whether to enroll a child in the academic year, since the school year begins in early June and late enrollment is generally not permitted. This suggests that the effects of cable on enrollment in many cases may not show up until at earliest the academic year starting in the calendar year after cable is introduced. To capture this and any other potential delayed (or increasing) effects of cable, our empirical analysis will analyze both the effect of having cable and years of cable access. Using these data, we follow a similar specification to that above, regressing log enrollment on whether the village has cable and years of cable access. To account for possible serial correlation, we use the Prais-Winston estimator. The controls include electrification (inferred from whether the school has electricity), population 23 and distance to the nearest town. 24 The primary results are in Panel A of Table VI. Although there is no on-introduction effect of cable (the coefficient on having cable is small and insignificant) for the fixed cohort and for children under 10 there is strong evidence that continued exposure to cable (years of access) increases enrollment. For the fixed cohort aged 6-7, the effect is large, with additional years increasing enrollment by 5 percent. For children aged 6-10, the effect is smaller, between one and two percent, but still statistically significant. Finally, consistent with the SARI data, we see no effect for the older group. We can see some visual evidence of the results in Table VI for the fixed aged cohort (including both sexes) in Figure VII. These graphs show the changes in enrollment over time, by date of cable adoption, relative to areas that never get cable. Although the figures lack the sharpness of the SARI figures, we do see evidence of increases in enrollment after cable is introduced The population data available in the DISE data are for children aged 6-14, which is a reliable proxy for overall population only under the assumption that dependency ratios are not systematically different across village types. 24 We attempted to include additional controls by merging our data with data from the Census, but differences in village names meant we would only be able to match half of the DISE villages. 25 Appendix Figures W.1 and W.2 show these graphs separated by sex. 20